CN116341845A - Automatic early warning and intelligent supplying method and system for sanitation vehicle resources - Google Patents
Automatic early warning and intelligent supplying method and system for sanitation vehicle resources Download PDFInfo
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Abstract
The invention provides an automatic early warning and intelligent replenishing method and system for environmental sanitation vehicle resources, comprising the following steps: the inspection vehicle runs on the road in real time, and acquires real-time state parameter information of the vehicle, and supply static information of a station and dynamic parameter information of station resources through the gateway layer of the vehicle networking; the vehicle intelligent route replenishment calculation engine is adopted to give vehicle proposal replenishment time and vehicle proposal replenishment path; according to the real-time traffic condition data of the road collected by the unmanned plane and the road inspection vehicle, carrying out multi-source heterogeneous data fusion to obtain a road topology traffic flow parameter set; and constructing a vehicle resource early warning model, and correcting the vehicle recommended replenishment time and the vehicle recommended replenishment path to obtain a vehicle early warning prompt and a vehicle optimal intelligent replenishment algorithm result based on global real-time traffic flow information. Aiming at the complexity and macroscopicity of the urban sanitation operation field, the invention improves the effectiveness of sanitation vehicle resource replenishment work by an intelligent replenishment method based on real-time traffic flow.
Description
Technical Field
The invention relates to the technical field of sanitation, in particular to an automatic early warning and intelligent supplying method and system for sanitation vehicle resources.
Background
With the deep progress of urban treatment, the continuous development of urban scale, and the large-scale sanitation of the city are important foundation for normal running of the city. However, due to the coupling and complexity of large-scale operation of cities, a large number of sudden and occasional sanitation events are generated every day to be processed (such as large-scale garbage discarding on road surfaces, road surface earthwork accumulation, road surface mess brought by traffic accidents, traffic auxiliary roads covered by industrial garbage and the like), and huge-scale sanitation normalization tasks are required to be executed every day. For both tasks, a significant amount of sanitation vehicle resources are required to perform. In addition, in the running process of the vehicle resources, the real-time state of the vehicle resources (such as water, electricity, garbage can allowance and other state parameters) needs to be continuously researched and judged, and an alarm is given when the resources are in or are about to be in a critical state; further, an optimal supply path of the vehicle-mounted resources can be provided; at present, the common practice in the sanitation industry is to send out a large number of vehicles to carry out operation, and the early warning of operation resources is given to operation executives to be manually responsible, so that a large number of manual experiences and temporary decisions exist, the vehicles often cannot reach the optimal resource replenishing effect due to the uncertainty of the manual experience decisions, and the replenishing path needs to be judged through manual experience, so that the dynamic replenishing range and precision are greatly limited; and, since the road traffic conditions are constantly changing dynamically, this increases the replenishment margin time and uncertainty of the optimal replenishment path.
The Chinese patent document with publication number CN115358589A discloses an automatic driving sanitation vehicle dispatching method, device, equipment and storage medium, wherein the method comprises the following steps: acquiring a plurality of task routes, vehicle data of an automatic driving sanitation vehicle to be scheduled and resource consumption conditions of the automatic driving sanitation vehicle to be scheduled in different operation modes; calculating a scheduling scheme with the shortest operation duration according to the task route, the resource consumption condition of the automatic driving sanitation vehicle to be scheduled in different operation modes and the vehicle data; and acquiring a task distribution result according to the scheduling scheme, and transmitting the task distribution result to a corresponding vehicle-mounted server through the cloud server, so that the automatic driving sanitation vehicle to be scheduled performs sanitation operation according to the task distribution result. The Chinese patent document with publication number of CN112663549A discloses an environmental sanitation truck operation path planning method based on road surface cleanliness detection, which comprises the following steps: step one, a positioning module and a route planning module are arranged in the sanitation truck, and the positioning module and the route planning module are connected with a remote control platform. And step two, transmitting road surface information required to be cleaned by the sanitation truck to the inside of a route planning module arranged in the sanitation truck through a remote control platform. And thirdly, a weight detection module is arranged in a dustbin on the sanitation truck, a water level detection module is arranged in a water tank, and the weight detection module and the water level detection module are electrically connected with an sanitation truck internal control system. And step four, the weight detection module detects the weight of the garbage inside the dustbin in real time, so as to judge whether the dustbin is full or not, and meanwhile, the water level detection module detects the water level condition inside the water tank in real time, so as to judge whether the water tank is lack of water or not. And fifthly, analyzing the space and the water level conditions inside the dustbin and the water tank detected by the weight detection module and the water level detection module in combination with the water supplementing point and the garbage dumping point which are arranged on the road surface through the control system, and selecting the nearest water supplementing point and the nearest garbage dumping point to supplement and dump garbage. According to the sanitation truck operation path planning method based on road surface cleanliness detection, the water level inside the water tank can be effectively detected through the water level detection module arranged inside the water tank, so that the actual distance traveled by the sanitation truck on the road surface is judged, the control system inside the sanitation truck can plan the shortest water source supplementing line, the water inside the water tank can be timely supplemented, the problem that water is needed to be added in places with far water adding points is avoided, the real-time residual capacity inside the dustbin is detected through the mutual cooperation of the weight detection module and the infrared pair-fire tubes, the position where garbage is dumped is comprehensively judged in advance, and the cleaning efficiency is improved.
With respect to the related art in the above, the inventors consider that the above method has the following problems: (1) The vehicle dynamic parameter information consideration points are limited to the garbage can allowance and water quantity, vehicle dynamic information such as electric quantity is not considered, and meanwhile static information of energy supplementing stations such as station water quantity, electric quantity and residual supplementing vehicle space is ignored, and the nearest supplementing station is selected, so that resource vacancy, queuing and the like of the supplementing station are easily caused; (2) The path planning is simply planned according to the self-vehicle resource condition and the nearest supply station, and the influence of the real-time traffic flow on the path is not considered, so that the energy supply efficiency is influenced. The above problems, such as the roughness of the manual judgment of the replenishment threshold, the determination of how to optimize the replenishment path, the need to consider the changes of the real-time road traffic flow when the replenishment scheme is prepared, are far from being effectively solved by the manual empirical decision.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an automatic early warning and intelligent replenishing method and system for environmental sanitation vehicle resources.
The invention provides an automatic early warning and intelligent replenishing method for environmental sanitation vehicle resources, which comprises the following steps:
an information acquisition step: the inspection vehicle runs on the road in real time, acquires real-time state parameter information of the vehicle through the vehicle networking gateway layer, and synchronously acquires static information of the supply station and dynamic parameter information of the station resource through the vehicle networking gateway layer;
a vehicle proposal step: based on the real-time state parameter information, the static information of the replenishment station and the dynamic parameter information of the station resource of the vehicle, a vehicle intelligent route replenishment calculation engine is adopted to give a vehicle proposal replenishment time and a vehicle proposal replenishment path;
traffic flow parameter collection: according to the real-time traffic condition data of the road collected by the unmanned plane and the road inspection vehicle, carrying out multi-source heterogeneous data fusion, and constructing the real-time traffic flow distribution condition of the global road to obtain a road topology traffic flow parameter set;
a vehicle advice correction step: based on road topology real-time traffic flow parameters, a vehicle resource early warning model is constructed, vehicle recommended replenishment time and vehicle recommended replenishment paths are corrected, and then vehicle early warning prompt and vehicle optimal intelligent replenishment algorithm results based on global real-time traffic flow information are obtained.
Preferably, the real-time state parameter information of the vehicle comprises the garbage can allowance, the water quantity and the electric quantity;
the static information of the replenishment site comprises longitude and latitude;
the site resource dynamic parameter information comprises site water quantity, electric quantity, garbage residual capacity and residual access supply vehicle space.
Preferably, the vehicle recommended replenishment time includes a time to interrupt a mission to make replenishment.
Preferably, the system further comprises an engine building step of: a vehicle intelligent route replenishment calculation engine is constructed, which is constructed based on a mixed integer nonlinear programming algorithm.
Preferably, in the traffic flow parameter collection step, video frames and point cloud frame data acquired by the unmanned aerial vehicle in real time through the sensor are uploaded to the cloud end through a network, video frames, point cloud frames and vehicle condition data acquired by the inspection vehicle are uploaded to the cloud end through a vehicle network gateway layer, multi-source heterogeneous data fusion is carried out, and the traffic flow real-time distribution condition of the whole road is constructed.
The invention provides an automatic early warning and intelligent supplying system for environmental sanitation vehicle resources, which comprises the following modules:
an information acquisition module: the inspection vehicle runs on the road in real time, acquires real-time state parameter information of the vehicle through the vehicle networking gateway layer, and synchronously acquires static information of the supply station and dynamic parameter information of the station resource through the vehicle networking gateway layer;
the vehicle suggestion module: based on the real-time state parameter information, the static information of the replenishment station and the dynamic parameter information of the station resource of the vehicle, a vehicle intelligent route replenishment calculation engine is adopted to give a vehicle proposal replenishment time and a vehicle proposal replenishment path;
a traffic flow parameter collection module: according to the real-time traffic condition data of the road collected by the unmanned plane and the road inspection vehicle, carrying out multi-source heterogeneous data fusion, and constructing the real-time traffic flow distribution condition of the global road to obtain a road topology traffic flow parameter set;
the vehicle proposal correction module: based on road topology real-time traffic flow parameters, a vehicle resource early warning model is constructed, vehicle recommended replenishment time and vehicle recommended replenishment paths are corrected, and then vehicle early warning prompt and vehicle optimal intelligent replenishment algorithm results based on global real-time traffic flow information are obtained.
Preferably, the real-time state parameter information of the vehicle comprises the garbage can allowance, the water quantity and the electric quantity;
the static information of the replenishment site comprises longitude and latitude;
the site resource dynamic parameter information comprises site water quantity, electric quantity, garbage residual capacity and residual access supply vehicle space.
Preferably, the vehicle recommended replenishment time includes a time to interrupt a mission to make replenishment.
Preferably, the system further comprises an engine building module: a vehicle intelligent route replenishment calculation engine is constructed, which is constructed based on a mixed integer nonlinear programming algorithm.
Preferably, in the traffic flow parameter set module, video frames and point cloud frame data acquired by the unmanned aerial vehicle in real time through the sensor are uploaded to the cloud end through a network, video frames, point cloud frames and vehicle condition data acquired by the inspection vehicle are uploaded to the cloud end through a vehicle network gateway layer, multi-source heterogeneous data fusion is carried out, and the traffic flow real-time distribution condition of the whole road is constructed.
Compared with the prior art, the invention has the following beneficial effects:
1. aiming at the complexity and macroscopicity of the urban sanitation operation field, the invention provides the automatic early warning and intelligent supplying method and system for the sanitation vehicle resources based on the real-time working condition, which can effectively solve the roughness problem of manual research and judgment in the sanitation resource scheduling and improve the effectiveness of the sanitation vehicle resource supplying work;
2. the scheme fully considers the influence of the real-time traffic flow, and gives the distribution parameters of the real-time traffic flow through the fusion of the multi-source heterogeneous data;
3. according to the invention, an AI algorithm is introduced to intelligently calculate the optimal replenishment time and the optimal replenishment path, so that the effectiveness of the sanitation vehicle resource replenishment work is improved, and the intelligent quasi-real-time scheduling of manpower, material resources and distribution is realized.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of system steps;
fig. 2 is a real-time traffic flow collection graph.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
The embodiment of the invention discloses an automatic early warning and intelligent replenishing method for environmental sanitation vehicle resources, which is shown in fig. 1 and 2, and a specific embodiment comprises the following steps:
i. an information acquisition step: in the real-time working process of the inspection vehicle, the cloud system continuously collects state information. Specifically, the real-time state parameter information (such as dynamic information of the surplus of the dustbin, the water quantity, the electric quantity and the like and static information of part of vehicles) of the vehicles is obtained through the gateway layer of the internet of vehicles, and in addition, the static information of the replenishment station and the dynamic parameter information of the station resources (such as longitude and latitude and the like, and the dynamic information such as the water quantity, the electric quantity, the residual capacity of the garbage and the residual access replenishment vehicle space and the like) are synchronously obtained through the gateway layer of the internet of vehicles.
Vehicle advice step: after the state information is collected to the cloud, a vehicle intelligent route replenishment calculation engine is constructed based on the vehicle real-time state information and the vehicle replenishment point and replenishment station global information, and the engine is constructed based on a mixed integer nonlinear programming algorithm. After extensive optimization calculations, vehicle recommended replenishment times (time to interrupt the mission to replenish) are given, and vehicle recommended replenishment paths (without consideration of suboptimal paths in real-time traffic flow conditions).
Traffic flow parameter set step: the method comprises the steps that video frames and point cloud frame data collected in real time by an unmanned aerial vehicle from a sensor are continuously uploaded to a cloud end through a 4G/5G network, video frames, point cloud frames and vehicle conditions collected by a patrol vehicle are uploaded to the cloud end through a vehicle network gateway layer, multi-source heterogeneous data fusion is carried out, the real-time traffic flow distribution condition of a global road is built, and a road topology traffic flow parameter set is given.
The multi-source heterogeneous data are fused, the traffic flow real-time distribution condition of the global road is built, and the road topology traffic flow parameter set is given. Inspection vehicle: the inspection vehicle is provided with a plurality of sensors, including a high-definition camera, a laser radar, a millimeter wave radar, global satellite positioning, an inertial measurement sensor and the like, wherein the laser radar and the millimeter wave radar can take the vehicle as the center to form regional radius radiation in the moving process of the inspection vehicle, and vehicle data in a radiation ring are collected; meanwhile, the high-definition cameras with multiple angles can record real-time scenes around the vehicle, and the real-time scenes are specifically divided into video streams and picture sets; the video stream is automatically recorded when the vehicle arrives near the intersection, stops after leaving the intersection, and the picture set is collected at fixed time, and is generally collected and photographed at intervals of 0.5 s. The video stream, the image set and the radar point cloud data are grouped according to the time stamp and are uploaded back to the cloud in real time.
And the cloud end returns according to the data of the inspection vehicle, and a traffic real-time flow is constructed on the grid map. The inspection vehicle data can continuously update the grid attribute information in the grid map, for example: average number of vehicles, average vehicle speed, average waiting time, etc. The data in the grid map becomes an initial input for a preliminary replenishment solution for the vehicle route, and the sanitation vehicle selects a replenishment site and a replenishment route based on the grid map data and the real-time data of each replenishment site. The choice of replenishment sites has influence factors: site open time, site resource inventory, site busyness, etc., the factors influencing the choice of replenishment routes are currently calculated based on the grid map data.
Data fusion: unmanned aerial vehicle: the unmanned aerial vehicle divides according to the region, and in general cases, one unmanned aerial vehicle is responsible for regional monitoring of 9-16 grid ranges, and the unmanned aerial vehicle cruises in a fixed track according to preset during monitoring, and continuously returns grid information in the region to the cloud platform.
Data fusion reasons (i.e., drawbacks of single type data input): 1. the unmanned aerial vehicle is limited by software and hardware, so that the unmanned aerial vehicle cannot be used as cruise vehicle in data collection, multiple types of large storage and high-precision sensing equipment are used, and the unmanned aerial vehicle is generally matched with only a high-definition camera, so that the data of the unmanned aerial vehicle are actually supplementary to the data of the cruise vehicle; 2. the unmanned aerial vehicle is easily influenced by external environment factors, such as heavy wind, heavy rain, heavy snow and other extreme weather conditions, the unmanned aerial vehicle works under larger interference, and a large amount of noise exists in the quality of data collection, so that multiple data sources are required to be input and fused, and the adverse effect of noise data is reduced; 3. the number of the cruising vehicles is limited, and real-time monitoring of the whole domain is difficult to realize. The cruise vehicle cannot realize omnibearing coverage on a large-area region due to the problems of cost, manpower and resources, so that additional tools are needed for data collection, and the unmanned aerial vehicle meets the requirements in various aspects of time, space, cost and the like. And in the area where the cruising vehicle is not focused, the data collected by the unmanned aerial vehicle is used, and the data integration of the cruising vehicle and the unmanned aerial vehicle is realized in the cruising vehicle checking area in a weight distribution mode.
For the cloud platform, the data integration result is that the cloud platform is on a basic layer of an actual map, a layer of grid map is built again, dynamic data for calculating traffic flow in real time is contained in the grid map, and the cloud platform adopts a mixed integer nonlinear programming algorithm to heuristically program an optimal supply path and supply sites based on the basic layer, the topological relation and the traffic flow data of the grid map. As shown in fig. 2.
Vehicle advice correction step: based on the road topology real-time traffic flow parameter set, constructing a final vehicle resource early warning model, and correcting vehicle recommended replenishment time and vehicle recommended replenishment paths; and finally, giving a vehicle early warning prompt and a vehicle optimal intelligent replenishment algorithm result based on the global real-time traffic flow information.
The vehicle resource early warning model can realize the following effects on the premise of the vehicle traffic flow aggregation step:
1. the sanitation vehicle uploads the data of the sanitation vehicle to the cloud platform, and the cloud platform can guide how the sanitation vehicle performs resource replenishment based on the current road traffic flow condition and the real-time state of each station. 2. In the process that the sanitation truck executes the issuing task in the automatic driving mode, the cloud platform can pay attention to the current vehicle resource remaining condition and resource replenishment consumption (namely, the guiding mode mentioned in the 1 st item) in real time, so that the cloud platform can actively predict whether the vehicle can complete the current task or not, and can return to a replenishment point or not after completing the task; if the risk exists, the cloud platform can actively generate a risk event, and then manual intervention is performed.
The vehicle early warning model is a check mechanism of the cloud platform for the automatic driving vehicle, and for all online vehicles, the cloud platform can pay attention to vehicle resource data in real time, and different thresholds are set for different types and models of automatic driving sanitation vehicles, such as single-corner animals (models): 100, haoke (model): 10, the number here indicates that the automatic driving vehicle returns data every x times, and the current vehicle optimally supplies the consumed resources are calculated, for example: the consumption of water, electricity, oil and garbage cans is combined with the resource data of the vehicle to perform subsequent feasibility analysis, and a greedy barrel algorithm is adopted at present, namely, all resources must meet the optimal replenishment requirement at the same time to consider that the vehicle can execute subsequent tasks, otherwise, a vehicle alarm event is generated, and meanwhile, the vehicle stops working to select the nearest parking spot to wait in situ. The vehicle alarm event is distributed to the current attendant by a billing system of the cloud platform, and meanwhile, related pushing of telephone and flyer books can be carried out.
The flow block diagram of all the steps is shown in fig. 1, and an automatic early warning and intelligent replenishment algorithm for sanitation vehicle resources based on real-time working conditions is constructed by taking the flow block diagram as a framework. The method comprises the steps of determining a vehicle resource model based on comprehensive real-time state parameters of a vehicle, information of a replenishment station and dynamic parameters of station resources together, carrying out early warning prompt on the vehicle, and carrying out preliminary optimal intelligent replenishment algorithm planning, wherein the planning comprises work interruption time and a path; the method combines the unmanned aerial vehicle acquisition point cloud data and the patrol vehicle dynamic data to perform multi-source heterogeneous data fusion, and gives out real-time traffic flow parameters; according to the invention, based on real-time traffic flow parameters, an AI algorithm is introduced to intelligently calculate the optimal replenishment time and the optimal replenishment path, so that the optimal planning of the energy replenishment path is realized.
The invention provides an automatic early warning and intelligent replenishment algorithm for sanitation vehicle resources based on real-time working conditions; firstly, acquiring real-time state parameter information (such as dynamic information of dustbin allowance, water quantity, electric quantity and the like) of a vehicle through a gateway layer of a vehicle network on the premise that a patrol vehicle runs on a road in real time, synchronously acquiring static information of a replenishment station and dynamic parameter information of station resources (such as longitude and latitude and the like, and dynamic information such as station water quantity, electric quantity, garbage residual capacity, residual access replenishment vehicle space and the like) through the gateway layer of the vehicle network, and then, based on the real-time state information of the vehicle and global information of a replenishment point/replenishment station, adopting a vehicle intelligent route replenishment calculation engine to give vehicle proposal replenishment time (time for interrupting a task to carry out replenishment) and vehicle proposal replenishment paths; on the other hand, multi-source heterogeneous data fusion is carried out according to the real-time traffic condition data of the roads collected by the unmanned plane and the road inspection vehicle, the real-time traffic flow distribution condition of the global roads is constructed, and the road topology traffic flow parameter set is given. Further, based on the road topology real-time traffic flow parameters, constructing a vehicle resource early warning model, and correcting vehicle recommended replenishment time and a vehicle recommended replenishment path; and finally, giving a vehicle early warning prompt and a vehicle optimal intelligent replenishment algorithm result based on the global real-time traffic flow information. According to the scheme, the influence of real-time traffic flow is fully considered, the distribution parameters of the real-time traffic flow are given through fusion of multi-source heterogeneous data, the AI algorithm is introduced to intelligently calculate the optimal replenishment time and the optimal replenishment path, so that the effectiveness of the sanitation vehicle resource replenishment work is improved, the manpower, material resources and distribution are intelligently scheduled in a quasi-real time mode, and the accuracy and predictability of decision making are improved.
The invention also provides an automatic early warning and intelligent replenishing system for the environmental sanitation vehicle resources, which can be realized by executing the flow steps of the automatic early warning and intelligent replenishing method for the environmental sanitation vehicle resources, namely, a person skilled in the art can understand the automatic early warning and intelligent replenishing method for the environmental sanitation vehicle resources as a preferred implementation mode of the automatic early warning and intelligent replenishing system for the environmental sanitation vehicle resources.
The system comprises the following modules:
an information acquisition module: the inspection vehicle runs on the road in real time, acquires real-time state parameter information of the vehicle through the vehicle networking gateway layer, and synchronously acquires static information of the replenishment station and dynamic parameter information of the station resource through the vehicle networking gateway layer. The real-time state parameter information of the vehicle comprises the residual quantity of the dustbin, the water quantity and the electric quantity; the static information of the replenishment site comprises longitude and latitude; the site resource dynamic parameter information comprises site water quantity, electric quantity, garbage residual capacity and residual access supply vehicle space.
The engine building module: a vehicle intelligent route replenishment calculation engine is constructed, which is constructed based on a mixed integer nonlinear programming algorithm.
The vehicle suggestion module: based on the real-time state parameter information, the static information of the replenishment station and the dynamic parameter information of the station resource, the vehicle intelligent route replenishment calculation engine is adopted to give the vehicle proposal replenishment time and the vehicle proposal replenishment path. The vehicle recommended replenishment time includes a time to interrupt a task to make replenishment.
A traffic flow parameter collection module: and carrying out multi-source heterogeneous data fusion according to the real-time traffic condition data of the road collected by the unmanned aerial vehicle and the road inspection vehicle, and constructing the real-time traffic flow distribution condition of the global road to obtain a road topology traffic flow parameter set. Video frames and point cloud frame data acquired by the unmanned aerial vehicle in real time through the sensor are uploaded to the cloud end through a network, video frames, point cloud frames and vehicle condition data acquired by the inspection vehicle are uploaded to the cloud end through a vehicle network gateway layer, multi-source heterogeneous data fusion is carried out, and the traffic flow real-time distribution condition of the global road is constructed.
The vehicle proposal correction module: based on road topology real-time traffic flow parameters, a vehicle resource early warning model is constructed, vehicle recommended replenishment time and vehicle recommended replenishment paths are corrected, and then vehicle early warning prompt and vehicle optimal intelligent replenishment algorithm results based on global real-time traffic flow information are obtained.
Those skilled in the art will appreciate that the invention provides a system and its individual devices, modules, units, etc. that can be implemented entirely by logic programming of method steps, in addition to being implemented as pure computer readable program code, in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units for realizing various functions included in the system can also be regarded as structures in the hardware component; means, modules, and units for implementing the various functions may also be considered as either software modules for implementing the methods or structures within hardware components.
The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the invention. The embodiments of the present application and features in the embodiments may be combined with each other arbitrarily without conflict.
Claims (10)
1. An automatic early warning and intelligent replenishing method for environmental sanitation vehicle resources is characterized by comprising the following steps:
an information acquisition step: the inspection vehicle runs on the road in real time, acquires real-time state parameter information of the vehicle through the vehicle networking gateway layer, and synchronously acquires static information of the supply station and dynamic parameter information of the station resource through the vehicle networking gateway layer;
a vehicle proposal step: based on the real-time state parameter information, the static information of the replenishment station and the dynamic parameter information of the station resource of the vehicle, a vehicle intelligent route replenishment calculation engine is adopted to give a vehicle proposal replenishment time and a vehicle proposal replenishment path;
traffic flow parameter collection: according to the real-time traffic condition data of the road collected by the unmanned plane and the road inspection vehicle, carrying out multi-source heterogeneous data fusion, and constructing the real-time traffic flow distribution condition of the global road to obtain a road topology traffic flow parameter set;
a vehicle advice correction step: based on road topology real-time traffic flow parameters, a vehicle resource early warning model is constructed, vehicle recommended replenishment time and vehicle recommended replenishment paths are corrected, and then vehicle early warning prompt and vehicle optimal intelligent replenishment algorithm results based on global real-time traffic flow information are obtained.
2. The method for automatically warning and intelligently replenishing the resources of the sanitation vehicle according to claim 1, wherein the real-time state parameter information of the vehicle comprises the surplus of the dustbin, the water quantity and the electric quantity;
the static information of the replenishment site comprises longitude and latitude;
the site resource dynamic parameter information comprises site water quantity, electric quantity, garbage residual capacity and residual access supply vehicle space.
3. The method for automatically warning and intelligently replenishing sanitation vehicle resources according to claim 1, wherein the recommended replenishment time of the vehicle comprises a time for interrupting a mission to replenish.
4. The method for automatically warning and intelligently replenishing sanitation vehicle resources according to claim 1, wherein the system further comprises the engine construction step of: a vehicle intelligent route replenishment calculation engine is constructed, which is constructed based on a mixed integer nonlinear programming algorithm.
5. The automatic early warning and intelligent replenishment method for sanitation vehicle resources according to claim 1 is characterized in that in the traffic flow parameter collection step, video frames and point cloud frame data acquired by an unmanned aerial vehicle in real time through a sensor are uploaded to a cloud end through a network, video frames, point cloud frames and vehicle condition data acquired by a patrol vehicle are uploaded to the cloud end through a vehicle network gateway layer, multi-source heterogeneous data fusion is carried out, and traffic flow real-time distribution conditions of a global road are constructed.
6. An automatic early warning and intelligent replenishing system for environmental sanitation vehicle resources is characterized by comprising the following modules:
an information acquisition module: the inspection vehicle runs on the road in real time, acquires real-time state parameter information of the vehicle through the vehicle networking gateway layer, and synchronously acquires static information of the supply station and dynamic parameter information of the station resource through the vehicle networking gateway layer;
the vehicle suggestion module: based on the real-time state parameter information, the static information of the replenishment station and the dynamic parameter information of the station resource of the vehicle, a vehicle intelligent route replenishment calculation engine is adopted to give a vehicle proposal replenishment time and a vehicle proposal replenishment path;
a traffic flow parameter collection module: according to the real-time traffic condition data of the road collected by the unmanned plane and the road inspection vehicle, carrying out multi-source heterogeneous data fusion, and constructing the real-time traffic flow distribution condition of the global road to obtain a road topology traffic flow parameter set;
the vehicle proposal correction module: based on road topology real-time traffic flow parameters, a vehicle resource early warning model is constructed, vehicle recommended replenishment time and vehicle recommended replenishment paths are corrected, and then vehicle early warning prompt and vehicle optimal intelligent replenishment algorithm results based on global real-time traffic flow information are obtained.
7. The automatic early warning and intelligent replenishment system for sanitation vehicle resources according to claim 6, wherein the real-time status parameter information of the vehicle comprises a dustbin allowance, water quantity and electric quantity;
the static information of the replenishment site comprises longitude and latitude;
the site resource dynamic parameter information comprises site water quantity, electric quantity, garbage residual capacity and residual access supply vehicle space.
8. The automated environmental sanitation vehicle resource warning and intelligent replenishment system of claim 6, wherein the vehicle recommended replenishment time comprises a time to interrupt a mission for replenishment.
9. The system for automatic early warning and intelligent replenishment of sanitation vehicle resources of claim 6, further comprising an engine building module: a vehicle intelligent route replenishment calculation engine is constructed, which is constructed based on a mixed integer nonlinear programming algorithm.
10. The system for automatically warning and intelligently supplementing sanitation vehicle resources according to claim 6, wherein in the traffic flow parameter collection module, video frames and point cloud frame data acquired by an unmanned aerial vehicle in real time through a sensor are uploaded to a cloud end through a network, video frames, point cloud frames and vehicle condition data acquired by a patrol vehicle are uploaded to the cloud end through a vehicle network gateway layer, multi-source heterogeneous data fusion is carried out, and traffic flow real-time distribution conditions of a global road are constructed.
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| CN117094535A (en) * | 2023-10-19 | 2023-11-21 | 深圳市能数科技有限公司 | Artificial intelligence-based energy supply management method and system |
| CN117094535B (en) * | 2023-10-19 | 2024-01-16 | 深圳市能数科技有限公司 | Artificial intelligence-based energy supply management method and system |
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